# coding=utf-8

import os
import tensorflow as tf
from PIL import Image
import numpy as np

# 旋转去0，80,40，200 batch
file_path = "/usr/data/比赛试题/比赛数据"
trade_all_labels = []
with open(os.path.join(file_path, 'train', 'train_labels.txt')) as f:
    for i in f:
        tmp = np.zeros([10])
        tmp[int(i)] = 1
        trade_all_labels.append(tmp)


# 将图片转化为必要格式
def translate_img(im):
    im_array = np.array(im)
    return (255 - np.array([j for vec in im_array for j in vec])) / 255


# 图像旋转
def rotateImAndTranslate(im):
    angle = (3 - np.random.randint(7)) * 5
    if angle == 0:
        return translate_img(im.rotate(angle))
    else:
        im_array = np.array(im)
        for r in [0, 1, 2, 3, 4, 5, 6, 7, 8, 9] + [18, 19, 20, 21, 22, 23, 24, 25, 26, 27]:
            for c in [0, 1, 2, 3, 4, 5, 6, 7, 8, 9] + [18, 19, 20, 21, 22, 23, 24, 25, 26, 27]:
                if im_array[r][c] == 0:
                    im_array[r][c] = 255
        return (255 - np.array([j for vec in im_array for j in vec])) / 255


# 定义一个读取trade batch的函数
def next_batch(num):
    trade_images = []
    trade_labels = []
    sample = np.random.choice(59000, num, replace=False)
    for i in sample:
        im = Image.open(os.path.join(file_path, 'train', 'TrainImage', 'TestImage_%d.bmp' % (i + 1)))
        # trade_images.append(rotateImAndTranslate(im))
        trade_images.append(translate_img(im))
        trade_labels.append(trade_all_labels[i])
        im.close()
    return np.array(trade_images), np.array(trade_labels), sample


# 定义一个读取预测数据的函数
def predict_img():
    predict_images = []
    for i in range(10000):
        im = Image.open(os.path.join(file_path, 'RTestImage', 'TestImage_%d.bmp' % (i + 1)))
        predict_images.append(translate_img(im))
        im.close()
    return np.array(predict_images)


# 定义一个读取测试数据的函数
def tes_batch():
    test_images = []
    test_labels = []
    for i in range(1000):
        im = Image.open(os.path.join(file_path, 'train', 'TrainImage', 'TestImage_%d.bmp' % (i + 1 + 59000)))
        test_images.append(translate_img(im))
        test_labels.append(trade_all_labels[i + 59000])
        im.close()
    return np.array(test_images), np.array(test_labels)


# 定义计算图
sess = tf.InteractiveSession()
# 定义占位符（表示输入输出）
x = tf.placeholder("float", shape=[None, 784])
y_ = tf.placeholder("float", shape=[None, 10])


# 定义权重初始化函数
def weight_variable(shape):
    initial = tf.truncated_normal(shape, stddev=0.1)
    return tf.Variable(initial)


def bias_variable(shape):
    initial = tf.constant(0.1, shape=shape)
    return tf.Variable(initial)


# 定义卷积和池化函数
def conv2d(x, W):
    return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')


def max_pool_2x2(x):
    return tf.nn.max_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')


# 定义第一层网络结构
W_conv1 = weight_variable([5, 5, 1, 40])
b_conv1 = bias_variable([40])
x_image = tf.reshape(x, [-1, 28, 28, 1])
h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)
h_pool1 = max_pool_2x2(h_conv1)

# 定义第二层卷积
W_conv2 = weight_variable([5, 5, 40, 80])
b_conv2 = bias_variable([80])
h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)
h_pool2 = max_pool_2x2(h_conv2)

# 密集连接层
W_fc1 = weight_variable([7 * 7 * 80, 1024])
b_fc1 = bias_variable([1024])
h_pool2_flat = tf.reshape(h_pool2, [-1, 7 * 7 * 80])
h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)

# Dropout
keep_prob = tf.placeholder("float")
h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)

# 输出层
W_fc2 = weight_variable([1024, 10])
b_fc2 = bias_variable([10])

y_conv = tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2)

# 训练和评估模型
cross_entropy = -tf.reduce_sum(y_ * tf.log(y_conv))
train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
correct_prediction = tf.equal(tf.argmax(y_conv, 1), tf.argmax(y_, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))
sess.run(tf.initialize_all_variables())

for i in range(16000):
    trade_images, trade_labels, sample = next_batch(200)
    if i % 100 == 0:
        train_accuracy = accuracy.eval(feed_dict={x: trade_images, y_: trade_labels, keep_prob: 1.0})
        print("step %d, training accuracy %g" % (i, train_accuracy))
        if train_accuracy < 0.5 and i > 10000:
            print('error:step %d, trade_images.size = %d, trade_labels.size = %d, trade_all_labels.len = %d, sample is '
                  % (i, trade_images.size, trade_labels.size, len(trade_all_labels)), sample)
    train_step.run(feed_dict={x: trade_images, y_: trade_labels, keep_prob: 0.5})

test_images, test_labels = tes_batch()
print("test accuracy %g" % accuracy.eval(feed_dict={x: test_images, y_: test_labels, keep_prob: 1.0}))

# 输出预测结果
with open("/home/yanghb/test_labels1.txt", "w+") as f:
    model_output = tf.argmax(y_conv, 1)
    outs = model_output.eval(feed_dict={x: predict_img(), keep_prob: 1.0})
    for out in outs:
        f.write(str(out) + '\n')

# 保存模型
saver = tf.train.Saver()
saver_path = '/home/yanghb/mnist_model1/20170502_1.ckpt'
saver.save(sess, saver_path)
print('Model saved in file: ', saver_path)
